Visualizing raw trial looking time
d_no_target <- d %>%
filter(trial_stimulus_type != "target")Looking time distribution
RAW
data_with_demog %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram(bins = 90) +
xlim(0, 9000)## Warning: Removed 72 rows containing non-finite values (stat_bin).## Warning: Removed 2 rows containing missing values (geom_bar).
data_with_demog %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram(bins = 90) +
xlim(0, 9000) +
facet_wrap(~subject)## Warning: Removed 72 rows containing non-finite values (stat_bin).## Warning: Removed 134 rows containing missing values (geom_bar).
AFTER EXCLUSION
d %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram(bins = 90) +
xlim(0, 6000)## Warning: Removed 2 rows containing missing values (geom_bar).
d %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram(bins = 90) +
xlim(0, 6000) +
facet_wrap(~subject)## Warning: Removed 84 rows containing missing values (geom_bar).
Exclude the target trial
d_no_target %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram(bins = 90) +
xlim(0, 6000)## Warning: Removed 2 rows containing missing values (geom_bar).
d_no_target %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram(bins = 90) +
xlim(0, 6000) +
facet_wrap(~subject)## Warning: Removed 84 rows containing missing values (geom_bar).
complexity difference
d %>%
ggplot(aes(x = trial_looking_time,
fill = trial_stimulus_complexity),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000)
d %>%
ggplot(aes(x = trial_looking_time,
fill = trial_stimulus_complexity),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000) +
facet_wrap(~subject)
block difference
d %>%
ggplot(aes(x = trial_looking_time,
fill = block),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000)
d %>%
ggplot(aes(x = trial_looking_time,
fill = block),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000) +
facet_wrap(~subject)
Complexity differences no target
d_no_target %>%
ggplot(aes(x = trial_looking_time,
fill = trial_stimulus_complexity),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000)
d_no_target %>%
ggplot(aes(x = trial_looking_time,
fill = trial_stimulus_complexity),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000) +
facet_wrap(~subject)
Block differences no target
d_no_target %>%
ggplot(aes(x = trial_looking_time,
fill = block),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000)
d_no_target %>%
ggplot(aes(x = trial_looking_time,
fill = block),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000) +
facet_wrap(~subject)## Warning: Groups with fewer than two data points have been dropped.## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning -
## Inf
Visualizing aggregarded looking time
d_sum_individual <- d %>%
group_by(subject, block) %>%
summarise(
mean_lt = mean(trial_looking_time, na.rm = TRUE),
sd = sd(trial_looking_time, na.rm = TRUE),
n = n(),
ci_range_95 = qt(1 - (0.05 / 2), n - 1) * (sd/sqrt(n)),
ci_ub = mean_lt + ci_range_95,
ci_lb = mean_lt - ci_range_95
)## `summarise()` regrouping output by 'subject' (override with `.groups` argument)d_sum <- d %>%
group_by(block) %>%
summarise(
mean_lt = mean(trial_looking_time, na.rm = TRUE),
sd = sd(trial_looking_time, na.rm = TRUE),
n = n(),
ci_range_95 = qt(1 - (0.05 / 2), n - 1) * (sd/sqrt(n)),
ci_ub = mean_lt + ci_range_95,
ci_lb = mean_lt - ci_range_95
)## `summarise()` ungrouping output (override with `.groups` argument)aggregated
d_sum %>% ggplot(aes(x = block, y = mean_lt)) +
geom_pointrange(aes(ymin = ci_lb, ymax = ci_ub)) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
individual
something weird happened
d_sum_individual %>%
ggplot(aes(x = block, y = mean_lt)) +
geom_pointrange(aes(ymin = ci_lb, ymax = ci_ub)) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
d_sum_individual %>%
ggplot(aes(x = block, y = mean_lt)) +
geom_pointrange(aes(ymin = ci_lb, ymax = ci_ub)) +
facet_wrap(~subject) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
weird ones
We can see three very weird ones: SS1604513317537 SS1604515995769 SS1604516882157
weird <- c("SS1604513317537",
"SS1604515995769",
"SS1604516660396")
d_sum_individual %>%
filter(subject %in% weird) %>%
ggplot(aes(x = block, y = mean_lt)) +
geom_pointrange(aes(ymin = ci_lb, ymax = ci_ub)) +
facet_wrap(~subject) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
d %>%
filter(subject %in% weird) %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram(bins = 90) +
facet_wrap(~subject) +
xlim(0, 6000)+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))## Warning: Removed 6 rows containing missing values (geom_bar).
Excluding weird ones
d_no_weird <- d %>%
filter(!(subject %in% weird))
d_no_weird_sum <- d_no_weird %>%
group_by(block) %>%
summarise(
mean_lt = mean(trial_looking_time, na.rm = TRUE),
sd = sd(trial_looking_time, na.rm = TRUE),
n = n(),
ci_range_95 = qt(1 - (0.05 / 2), n - 1) * (sd/sqrt(n)),
ci_ub = mean_lt + ci_range_95,
ci_lb = mean_lt - ci_range_95
)## `summarise()` ungrouping output (override with `.groups` argument)d_no_weird_sum %>% ggplot(aes(x = block, y = mean_lt)) +
geom_pointrange(aes(ymin = ci_lb, ymax = ci_ub)) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
# Basic model
basic_m <- lmer(log(trial_looking_time) ~ trial_stimulus_type * block + (1|subject),
data = d)
summary(basic_m)## Linear mixed model fit by REML ['lmerMod']
## Formula: log(trial_looking_time) ~ trial_stimulus_type * block + (1 |
## subject)
## Data: d
##
## REML criterion at convergence: 10616.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3471 -0.5241 -0.1523 0.3587 5.4042
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.1001 0.3164
## Residual 0.2227 0.4719
## Number of obs: 7769, groups: subject, 42
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 6.218527 0.050443
## trial_stimulus_typedeviant 0.219409 0.030670
## trial_stimulus_typetarget 0.613224 0.031048
## blockall_simple 0.006138 0.017736
## blockmixed_complex_deviant 0.027101 0.017838
## blockmixed_simple_deviant 0.013656 0.018082
## trial_stimulus_typedeviant:blockall_simple 0.012400 0.043358
## trial_stimulus_typetarget:blockall_simple 0.042841 0.043881
## trial_stimulus_typedeviant:blockmixed_complex_deviant 0.015950 0.043425
## trial_stimulus_typetarget:blockmixed_complex_deviant 0.110322 0.044058
## trial_stimulus_typedeviant:blockmixed_simple_deviant 0.040369 0.044142
## trial_stimulus_typetarget:blockmixed_simple_deviant 0.166104 0.044662
## t value
## (Intercept) 123.277
## trial_stimulus_typedeviant 7.154
## trial_stimulus_typetarget 19.751
## blockall_simple 0.346
## blockmixed_complex_deviant 1.519
## blockmixed_simple_deviant 0.755
## trial_stimulus_typedeviant:blockall_simple 0.286
## trial_stimulus_typetarget:blockall_simple 0.976
## trial_stimulus_typedeviant:blockmixed_complex_deviant 0.367
## trial_stimulus_typetarget:blockmixed_complex_deviant 2.504
## trial_stimulus_typedeviant:blockmixed_simple_deviant 0.915
## trial_stimulus_typetarget:blockmixed_simple_deviant 3.719
##
## Correlation of Fixed Effects:
## (Intr) trl_stmls_typd trl_stmls_typt blckl_
## trl_stmls_typd -0.101
## trl_stmls_typt -0.100 0.164
## blckll_smpl -0.175 0.288 0.284
## blckmxd_cm_ -0.177 0.286 0.283 0.495
## blckmxd_sm_ -0.172 0.282 0.279 0.489
## trl_stmls_typd:_ 0.072 -0.707 -0.116 -0.409
## trl_stmls_typt:_ 0.071 -0.116 -0.707 -0.404
## trl_stmls_typdvnt:blckmxd_c_ 0.071 -0.706 -0.116 -0.203
## trl_stmls_typtrgt:blckmxd_c_ 0.071 -0.116 -0.705 -0.200
## trl_stmls_typdvnt:blckmxd_s_ 0.070 -0.695 -0.114 -0.200
## trl_stmls_typtrgt:blckmxd_s_ 0.070 -0.114 -0.695 -0.198
## blckmxd_c_ blckmxd_s_ trl_stmls_typd:_
## trl_stmls_typd
## trl_stmls_typt
## blckll_smpl
## blckmxd_cm_
## blckmxd_sm_ 0.490
## trl_stmls_typd:_ -0.203 -0.200
## trl_stmls_typt:_ -0.200 -0.197 0.165
## trl_stmls_typdvnt:blckmxd_c_ -0.406 -0.200 0.500
## trl_stmls_typtrgt:blckmxd_c_ -0.402 -0.197 0.082
## trl_stmls_typdvnt:blckmxd_s_ -0.199 -0.406 0.491
## trl_stmls_typtrgt:blckmxd_s_ -0.197 -0.401 0.081
## trl_stmls_typt:_ trl_stmls_typdvnt:blckmxd_c_
## trl_stmls_typd
## trl_stmls_typt
## blckll_smpl
## blckmxd_cm_
## blckmxd_sm_
## trl_stmls_typd:_
## trl_stmls_typt:_
## trl_stmls_typdvnt:blckmxd_c_ 0.082
## trl_stmls_typtrgt:blckmxd_c_ 0.499 0.164
## trl_stmls_typdvnt:blckmxd_s_ 0.081 0.491
## trl_stmls_typtrgt:blckmxd_s_ 0.492 0.081
## trl_stmls_typtrgt:blckmxd_c_
## trl_stmls_typd
## trl_stmls_typt
## blckll_smpl
## blckmxd_cm_
## blckmxd_sm_
## trl_stmls_typd:_
## trl_stmls_typt:_
## trl_stmls_typdvnt:blckmxd_c_
## trl_stmls_typtrgt:blckmxd_c_
## trl_stmls_typdvnt:blckmxd_s_ 0.080
## trl_stmls_typtrgt:blckmxd_s_ 0.490
## trl_stmls_typdvnt:blckmxd_s_
## trl_stmls_typd
## trl_stmls_typt
## blckll_smpl
## blckmxd_cm_
## blckmxd_sm_
## trl_stmls_typd:_
## trl_stmls_typt:_
## trl_stmls_typdvnt:blckmxd_c_
## trl_stmls_typtrgt:blckmxd_c_
## trl_stmls_typdvnt:blckmxd_s_
## trl_stmls_typtrgt:blckmxd_s_ 0.164